A Surveillance Video Anomaly Detection Method Based on Multi-region Variable Scale 3d-hof

Abstract

The invention discloses a monitoring video anomaly detection method based on multi-region variable-scale 3D-HOF. Firstly, the monitoring video is obtained as an input, and the video is partitioned, and then the variable-scale 3D-HOF features and optical flow direction information entropy in each partition are extracted. , and combined into the final detection features, and finally use the sparse combination learning algorithm to learn an initial sparse combination set in each partition, judge whether the new data is abnormal through the reconstruction error, and use the normal data to update the sparse combination set online. The application of the present invention not only solves the problem of perspective deformation in surveillance video, but also makes full use of the difference of motion information in different optical flow amplitude intervals to obtain more accurate motion speed information. The invention is suitable for abnormal detection of monitoring video, has low calculation complexity, accurate detection result and good algorithm robustness. The invention has wide application in the technical field of video analysis.

Description

technical field [0001] The invention belongs to the technical field of video analysis, and in particular relates to a monitoring video anomaly detection method based on multi-region variable-scale 3D-HOF, which is used for detecting abnormal objects and motion patterns in the monitoring video. Background technique [0002] Surveillance video anomaly detection is an important research direction in the field of video analysis technology. It has broad application prospects in scenes such as riot detection in public places, fare evasion detection at subway station entrances, fire warning, and intrusion monitoring. [0003] At present, most anomaly detection methods learn the model of the normal appearance and motion pattern of the object from the training video, and perform anomaly detection based on the established model, but rarely consider the position information of the object in the surveillance video on the appearance and motion pattern. Impact. Due to the perspective dis...

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Problems solved by technology

[0005] The problem to be solved by the present invention is: in the abnormal detection technology of surveillance video, if the problem of perspective deformation is not considered, the abnormal motion in the distance will be misjudged as the normal motion in the vicinity, resulting in missed detection; while the existing solution to the problem of perspective deformation The anomaly detection method does not consider the relationship between the whole and the part in the video, which will lead to false detection

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